Two-Stream 3-D convNet Fusion for Action Recognition in Videos With Arbitrary Size and Length
Abstract
3-D convolutional neural networks (3-D-convNets) have been very recently proposed for action recognition in videos, and promising results are achieved. However, existing 3-D-convNets has two “artificial” requirements that may reduce the quality of video analysis: 1) It requires a fixed-sized (e.g., 112 $\times$ 112) input video; and 2) most of the 3-D-convNets require a fixed-length input (i.e., video shots with fixed number of frames). To tackle these issues, we propose an end-to-end pipeline named Two-stream 3-D-convNet Fusion, which can recognize human actions in videos of arbitrary size and length using multiple features. Specifically, we decompose a video into spatial and temporal shots. By taking a sequence of shots as input, each stream is implemented using a spatial temporal pyramid pooling (STPP) convNet with a long short-term memory (LSTM) or CNN-E model, softmax scores of which are combined by a late fusion. We devise the STPP convNet to extract equal-dimensional descriptions for each variable-size shot, and we adopt the LSTM/CNN-E model to learn a global description for the input video using these time-varying descriptions. With these advantages, our method should improve all 3-D CNN-based video analysis methods. We empirically evaluate our method for action recognition in videos and the experimental results show that our method outperforms the state-of-the-art methods (both 2-D and 3-D based) on three standard benchmark datasets (UCF101, HMDB51 and ACT datasets).
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DH yields unsatisfactory performance on all datasets.
“It is also worth pointing out that DH [35], another deep-based hashing method in those baselines, yields unsatisfactory performance on all datasets, where three speculations are illustrated for those results as follows.”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [35]
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“Wang et al. [33] propose two-stream 3D Con-vNets Fusion to recognize actions of arbitrary size and length by using spatial temporal pyramid pooling (STPP) with a LSTM or CNN model to extract multi-size descriptions and learn global representation for each input video.”
MethodSelf-citationLiu, K., Gao, L., Khan, N., Qi, L., & Guan, L. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.2974323 · Cited by 64 · see citing works
“In this paper, motivated by classic two-stream CNN for action recognition in [22], [42], which takes appearance information as static feature stream and takes optical flow as dynamic feature stream.”
MethodSelf-citationAutomated TB classification using ensemble of deep architectures
Hooda, R., Mittal, A., & Sofat, S. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-07984-5 · Cited by 61 · see citing works
Deep learning approaches for seizure video analysis: A review
Ahmedt‐Aristizabal, D., Armin, M. A., Hayder, Z., Garcia‐Cairasco, N., Petersson, L., Fookes, C., Denman, S., & McGonigal, A. (2024) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2024.109735 · Cited by 63 · see citing works
Human Activity Recognition Using Cascaded Dual Attention CNN and Bi-Directional GRU Framework
Ullah, H., & Munir, A. (2023) · Journal of Imaging · doi:10.3390/jimaging9070130 · Cited by 64 · see citing works
“The rest of comparative methods including STPP+LSTM [67], optical flow + multi-layer LSTM [38], TSN [68], IP-LSTM [69], deep autoencoder [59], TS-LSTM + temporal-inception [70], HATNet [71], correlational CNN+LSTM [72], STDN [56], DB-LSTM+SSPF [39], DS-GRU [43], TCLC [73], and semi-supervised…”
BackgroundMethodInfluentialTwo-Stream Action Recognition-Oriented Video Super-Resolution
Zhang, H., Liu, D., & Xiong, Z. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00889 · Cited by 62 · see citing works
“Later on, C3D features and 3D CNN architectures [4, 35, 36, 39] appeared.”
BackgroundYogaNet: 3-D Yoga Asana Recognition Using Joint Angular Displacement Maps With ConvNets
Maddala, T. K. K., Kishore, P. V. V., Kumar, E. K., & Dande, A. K. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2904880 · Cited by 58 · see citing works
“[22] used an end-to-end, twostream CNN architecture for action videos of arbitrary length and size, which can be used on raw video data.”
MethodFused GRU with semantic-temporal attention for video captioning
Gao, L., Wang, X., Song, J., & Liu, Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.06.096 · Cited by 56 · see citing works
Self-citationPredictively encoded graph convolutional network for noise-robust skeleton-based action recognition
Yoon, Y., Yu, J., & Jeon, M. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02487-z · Cited by 56 · see citing works
3D-CNN-Based Fused Feature Maps with LSTM Applied to Action Recognition
Arif, S., Wang, J., Hassan, T. U., & Fei, Z. (2019) · Future Internet · doi:10.3390/fi11020042 · Cited by 55 · see citing works
Ravi, S., Maloji, S., Kishore, P. V. V., Kumar, E. K., Kumar, M. T. K., & Kumar, D. A. (2019) · Journal of Computer Languages · doi:10.1016/j.cola.2019.04.002 · Cited by 53 · see citing works
Serpush, F., & Rezaei, M. (2021) · SN Computer Science · doi:10.1007/s42979-021-00484-0 · Cited by 53 · see citing works
LSTM with bio inspired algorithm for action recognition in sports videos
Chen, J., Samuel, R. D. J., & Poovendran, P. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104214 · Cited by 52 · see citing works
Exposing Deepfake Face Forgeries With Guided Residuals
Guo, Z., Yang, G., Chen, J., & Sun, X. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3237169 · Cited by 50 · see citing works
“For image forensics, fusing two or more types of features can improve detection accuracy and robustness [39].”
MethodSelf-citationAhmad, T., & Wu, J. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3249152 · Cited by 52 · see citing works
“D CNNs, pooling schemes, and temporal modeling [31], [32], [33], [34], [35].”
BackgroundDense Semantics-Assisted Networks for Video Action Recognition
Luo, H., Lin, G., Yao, Y., Tang, Z., Wu, Q., & Hua, X. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3100842 · Cited by 49 · see citing works
Deep Self-Taught Hashing for Image Retrieval
Liu, Y., Song, J., Zhou, K., Yan, L., Liu, L., Zou, F., & Shao, L. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2822781 · Cited by 48 · see citing works
A Psychologically Inspired Fuzzy Cognitive Deep Learning Framework to Predict Crowd Behavior
Varghese, E. B., Thampi, S. M., & Berretti, S. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.2987021 · Cited by 47 · see citing works
Bidirectional Posture-Appearance Interaction Network for Driver Behavior Recognition
Tan, M., Ni, G., Liu, X., Zhang, S. S., Wu, X., Wang, Y., & Zeng, R. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3123127 · Cited by 47 · see citing works
“Here, we follow [40]–[42] to use the outputs of two”
MethodSelf-citationHussain, A., Khan, S. U., Khan, N., Shabaz, M., & Baik, S. W. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107218 · Cited by 55 · see citing works
Hussain, A., Khan, S. U., Rida, I., Khan, N., & Baik, S. W. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102211 · Cited by 44 · see citing works
Residual LSTM Attention Network for Object Tracking
Kim, H., & Park, R. (2018) · IEEE Signal Processing Letters · doi:10.1109/lsp.2018.2835768 · Cited by 44 · see citing works
“as video analysis including action recognition [12], [13], video captioning [14], [15], and video hashing [16].”
MethodLearning Composite Latent Structures for 3D Human Action Representation and Recognition
Wei, P., Sun, H., & Zheng, N. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2897902 · Cited by 42 · see citing works
“In recent years, neural network and deep learning methods have been extensively used in action recognition and achieved impressive results [36]–[46].”
Generalized zero-shot learning for action recognition with web-scale video data
Liu, K., Liu, W., Ma, H., Huang, W., & Dong, X. (2018) · World Wide Web · doi:10.1007/s11280-018-0642-6 · Cited by 41 · see citing works
“Many works [48, 53, 58] explore to utilize 3D convolutional operator to extract the motion features of videos.”
BackgroundEfficient Image Hashing with Geometric Invariant Vector Distance for Copy Detection
Liu, S., & Huang, Z. (2019) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3355394 · Cited by 41 · see citing works
“More recently, some learning-based hashing methods have been proposed, which includes machine learning hashing [33, 42–45] and deep learning hashing [46–49].”
BackgroundMethodSpatial–temporal pooling for action recognition in videos
Wang, J., Shao, Z., Huang, X., Lü, T., Zhang, R., & Lv, X. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.071 · Cited by 41 · see citing works
Aligning Correlation Information for Domain Adaptation in Action Recognition
Xu, Y., Cao, H., Mao, K., Chen, Z., Xie, L., & Yang, J. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3212909 · Cited by 40 · see citing works
“One of which utilizes the two-stream structure [30], [31], [32], [33], [34], [35], [36], extracting video features through CNNs from both optical flow and RGB inputs.”
MethodText-instance graph: Exploring the relational semantics for text-based visual question answering
Li, X., Wu, B., Song, J., Gao, L., Zeng, P., & Gan, C. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108455 · Cited by 39 · see citing works
Self-citationDastbaravardeh, E., Askarpour, S., Anari, M. S., & Rezaee, K. (2024) · International Journal of Intelligent Systems · doi:10.1155/2024/1052344 · Cited by 41 · see citing works
“Feature extraction is sometimes achieved by creating a skeleton from the video; however, the information gained from the skeleton is sometimes discarded, making the method less robust and accurate [37, 45, 89, 90].”
BackgroundDevaraj, J., Ganesan, S., Elavarasan, R. M., & Subramaniam, U. (2021) · Applied Sciences · doi:10.3390/app11094129 · Cited by 39 · see citing works
“A deep convolutional neural network called visual geometry group VGG 19, a successor of AlexNet, is used to classify the weather events [53].”
MethodHussain, A., Khan, S. U., Khan, N., Ullah, W., Alkhayyat, A. H., Alharbi, M., & Baik, S. W. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2023.11.017 · Cited by 39 · see citing works
Temporal Action Localization in Untrimmed Videos Using Action Pattern Trees
Song, H., Wu, X.-X., Zhu, B., Wu, Y., Chen, M., & Jia, Y.-D. (2019) · IEEE transactions on multimedia · doi:10.1109/tmm.2018.2866370 · Cited by 33 · see citing works
“HUMAN action understanding has been an active topic in computer vision with many applications including video surveillance, human computer interaction, sports video analysis, and video retrieval [7], [15], [38], [41], [47].”
BackgroundOn fusing the latent deep CNN feature for image classification
Liu, X., Zhang, R., Meng, Z., Hong, R., & Liu, G. (2018) · World Wide Web · doi:10.1007/s11280-018-0600-3 · Cited by 35 · see citing works
“The deep learning based video understanding methods [14, 52, 55] utilize novel fusion frameworks combining both the early fusion and late fusion strategies, and show great performance comparing to state of the art approaches.”
MethodSelf-citationA Hybrid Transformer Framework for Efficient Activity Recognition Using Consumer Electronics
Hussain, A., Khan, S. U., Khan, N., Bhatt, M. W., Farouk, A., Bhola, J., & Baik, S. W. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3373824 · Cited by 34 · see citing works
“…[13] 73.7 92.2 - T-Inception [28] 67.5 93.9 - OPLSTM [16] 72.21 94.45 94.9 LTF [29] 68.2 91.4 - STDAN + RGB [30] 60.4 91.0 AESVM [31] 70.3 94.33 96.4 3DCNN+Fusion 3DCF [32] 70.3 92.6 - Pseudo-3DCNN P3DCNN [33] - 93.7 - MiCT-3DCNN M3DCT [34] 70.5 94.7 - ViT-LSTM [12] 73.714 - 96.144 CDG [14] 72.”
InfluentialContent-based image retrieval via a hierarchical-local-feature extraction scheme
Jian, M., Yin, Y., Dong, J., & Lam, K. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6122-2 · Cited by 34 · see citing works
“In the big data era, content-based image retrieval (CBIR) has been a hot research topic in the last few years, because of its many practical applications, including in the fields of multimedia management and analysis [5, 40], biomedical image retrieval [43], dermatological lesions analysis [21], intelligent video surv…”
Action Recognition in Videos Using Pre-Trained 2D Convolutional Neural Networks
Kim, J.-H., & Won, C. S. (2020) · IEEE Access · doi:10.1109/access.2020.2983427 · Cited by 34 · see citing works
“In [17], the two-stream CNN structure was updated to support the videos with arbitrary size and length.”
MethodGan, H., Li, S., Ou, M., Yang, X., Huang, B., Liu, K., & Xue, Y. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106384 · Cited by 34 · see citing works
Duan, C., & Zhang, T. (2020) · IEEE Access · doi:10.1109/access.2020.3025165 · Cited by 33 · see citing works
“Through the comparison, we selected the appropriate fusion strategy and fusion layer, which can help us achieve better accuracy [32], [33].”
MethodLearning Semantic-Aware Spatial-Temporal Attention for Interpretable Action Recognition
Fu, J., Gao, J., & Xu, C. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3137023 · Cited by 33 · see citing works
Question-Led object attention for visual question answering
Gao, L., Cao, L., Xu, X., Shao, J., & Song, J. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.11.102 · Cited by 32 · see citing works
Self-citationCascade multi-head attention networks for action recognition
Wang, J., Peng, X., & Qiao, Y. (2020) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2019.102898 · Cited by 33 · see citing works
“Two-stream 3D-convNet Fusion is proposed to recognize actions in arbitrary size and length videos with multiple features (Wang et al., 2018a).”
BackgroundRen, Z., Zhang, Q., Cheng, J. S., Hao, F., & Gao, X. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.12.020 · Cited by 31 · see citing works
Deep learning methods for single camera based clinical in-bed movement action recognition
Karácsony, T., Jeni, L. A., la Torre, F. D., & Cunha, J. P. S. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.104928 · Cited by 30 · see citing works
“To further enhance the capture of long-term connections a 3D CNN-LSTM architecture was proposed [162].”
BackgroundFully Convolutional Network for Multiscale Temporal Action Proposals
Guo, D., Li, W., & Fang, X. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2839534 · Cited by 30 · see citing works
“In recent years, we have witnessed the rapid growth of various deep learning methods applied to video recognition [19]–[21].”
BackgroundSpatial–Temporal Context-Aware Online Action Detection and Prediction
Huang, J., Li, N., Li, T., Liu, S., & Li, G. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2923712 · Cited by 30 · see citing works
“[21] and [22] utilize a two-stream structue,”
BackgroundDeep Multi-Kernel Convolutional LSTM Networks and an Attention-Based Mechanism for Videos
Agethen, S., & Hsu, W. H. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2932564 · Cited by 30 · see citing works
“by many works for action recognition [12][13].”
Deep Gesture Video Generation With Learning on Regions of Interest
Cui, R., Cao, Z. L., Pan, W., Zhang, C., & Wang, J. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2960700 · Cited by 30 · see citing works
Weakly Semantic Guided Action Recognition
Yu, T., Wang, L., Da, C., Gu, H., Xiang, S., & Pan, C. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2907060 · Cited by 28 · see citing works
Order-aware convolutional pooling for video based action recognition
Wang, P., Liu, L., Shen, C., & Shen, H. T. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.03.002 · Cited by 29 · see citing works
Self-citationLiang, C., Liu, D., Qi, L., & Guan, L. (2020) · IEEE Access · doi:10.1109/access.2020.2976496 · Cited by 28 · see citing works
“Recent work [56] a 3D CNN fusion strategy is proposed by combing the softmax scores for action recognition with arbitrary length.”
MethodIntelligent Video Analytics for Human Action Recognition: The State of Knowledge
Kulbacki, M., Segen, J., Chaczko, Z., Rozenblit, J. W., Kulbacki, M., Klempous, R., & Wojciechowski, K. W. (2023) · Sensors · doi:10.3390/s23094258 · Cited by 29 · see citing works
“Several publicly available datasets have also been widely used in the research community to evaluate the performance of IVA algorithms.”
MethodModeling transformer architecture with attention layer for human activity recognition
Pareek, G., Nigam, S., & Singh, R. K. (2024) · Neural Computing and Applications · doi:10.1007/s00521-023-09362-7 · Cited by 28 · see citing works
“Wang et al. [20] introduced a two-stream 3D convNet fusion for action recognition in videos of arbitrary size and length, emphasizing the importance of flexibility in model design.”
Srihari, D., Kishore, P. V. V., Kumar, E. K., Kumar, D. A., Kumar, M. T. K., Prasad, M. V. D., & Prasad, C. R. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08588-9 · Cited by 26 · see citing works
“In this era of deep nets, multi-channel CNNs were introduced with RGB stream and depth stream separately as input to a CNN [44].”
MethodA New Robust Reference Image Hashing System
Singh, S. P., Bhatnagar, G., & Singh, A. K. (2021) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2021.3050435 · Cited by 26 · see citing works
Attention-based multi-modal fusion for improved real estate appraisal: a case study in Los Angeles
Bin, J., Gardiner, B., Liu, Z., & Li, E. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-07895-5 · Cited by 26 · see citing works
Adaptation-Oriented Feature Projection for One-shot Action Recognition
Zou, Y., Shi, Y., Shi, D., Wang, Y., Liang, Y., & Tian, Y. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.2972128 · Cited by 25 · see citing works
“Furthermore, [30] improved this model to recognize actions with arbitrary image sizes and video lengths.”
BackgroundSpatiotemporal Saliency Representation Learning for Video Action Recognition
Kong, Y., Wang, Y., & Li, A. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3066775 · Cited by 25 · see citing works
A Semantic and Motion-Aware Spatiotemporal Transformer Network for Action Detection
Korban, M., Youngs, P. A., & Acton, S. T. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3377192 · Cited by 25 · see citing works
Large scale image tamper detection and restoration
Sarkar, D., Palit, S., Som, S., & Dey, K. N. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08669-0 · Cited by 24 · see citing works
Zhang, Q., Zhou, L., Zhang, T., & Zhang, D.-H. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7180-9 · Cited by 23 · see citing works
“In [24], an end-to-end framework of Two-stream 3D convNet Fusion was proposed, which can recognize human actions in videos of arbitrary size and length using multiple features, and the proposed scheme enhanced the performance of CNNbased video analysis algorithm.”
MethodVideo sketch: A middle-level representation for action recognition
Zhang, X., Huang, Y., Mi, Y., Pei, Y., Zou, Q., & Wang, S. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01905-y · Cited by 23 · see citing works
“Wang et al. [28] present a two-stream 3D ConvNet fusion framework by considering both appearance and optical flows, which solves the issues of fixed-sized and fixed-length input video.”
BackgroundSpiking neural network based on joint entropy of optical flow features for human action recognition
Berlin, S. J., & John, M. (2020) · The Visual Computer · doi:10.1007/s00371-020-02012-2 · Cited by 25 · see citing works
Kasban, H., & Salama, D. H. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08100-3 · Cited by 22 · see citing works
Unsupervised densely attention network for infrared and visible image fusion
Li, Y., Wang, J., Miao, Z., & Wang, J. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09301-x · Cited by 22 · see citing works
Attention-Driven Appearance-Motion Fusion Network for Action Recognition
Liu, S., & Ma, X. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3148588 · Cited by 22 · see citing works
“…havebeendevelopedforhumanactionrecognition[6]:(1)2D ConvNets with temporal models on top such as LSTM [7], [8], temporal convolution [9], sparse sampling and aggregation [10], and attention modeling [11], [12], (2) 3D ConvNets [13], and (3) two-stream ConvNets [14], [15].”
BackgroundA Multi-Modal Transformer network for action detection
Korban, M., Youngs, P. A., & Acton, S. T. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109713 · Cited by 22 · see citing works
“[21] introduced a 3-”
Semantic-filtered Soft-Split-Aware video captioning with audio-augmented feature
Xu, Y., Yang, J., & Mao, K. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.05.027 · Cited by 22 · see citing works
Development of Compound Fault Diagnosis System for Gearbox Based on Convolutional Neural Network
Lin, M.-C., Han, P.-Y., Fan, Y.-H., & Li, C.-H. G. (2020) · Sensors · doi:10.3390/s20216169 · Cited by 22 · see citing works
“After that, CNN related research began to flourish in various fields, including object recognition [7,8], portrait recognition [9,10], action recognition [11,12], etc.”
Background
- Authors
- Wang, X., Gao, L., Wang, P., Sun, X., & Liu, X.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- The University of Adelaide (AU)
- Harbin Institute of Technology (CN)
- Beihang University (CN)
- Venue
- IEEE Transactions on Multimedia
- Published
- 2018-03
- Volume
- 20
- Issue
- 3
- Pages
- 634-644
- ISSN
- 1520-9210 (ISSN-L)
- 1941-0077
- OpenAlex
- W2751445731
- Cited by
- 263
- References
- 46
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 12 · 2023: 24 · 2022: 23 · 2021: 44 · 2020: 47 · 2019: 60 · 2018: 42 · 2017: 1 · 2012: 1
- FWCI
- 17.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Multimedia (publishedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- Funders
- Fundamental Research Funds for the Central Universities (ZYGX2014J063, ZYGX2014Z007)
- National Natural Science Foundation of China (61502080, 61632007)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- 3D convolutional neural networks, long short-term memory, decision fusion
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